{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 题目一"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odict_keys(['600000.XSHG', '600016.XSHG', '600028.XSHG', '600029.XSHG', '600030.XSHG', '600036.XSHG', '600048.XSHG', '600050.XSHG', '600100.XSHG', '600104.XSHG', '600111.XSHG', '600340.XSHG', '600485.XSHG', '600518.XSHG', '600519.XSHG', '600547.XSHG', '600606.XSHG', '600837.XSHG', '600887.XSHG', '600919.XSHG', '600958.XSHG', '600999.XSHG', '601006.XSHG', '601088.XSHG', '601166.XSHG', '601169.XSHG', '601186.XSHG', '601198.XSHG', '601211.XSHG', '601229.XSHG', '601288.XSHG', '601318.XSHG', '601328.XSHG', '601336.XSHG', '601390.XSHG', '601398.XSHG', '601601.XSHG', '601628.XSHG', '601668.XSHG', '601688.XSHG', '601766.XSHG', '601788.XSHG', '601800.XSHG', '601818.XSHG', '601857.XSHG', '601881.XSHG', '601901.XSHG', '601985.XSHG', '601988.XSHG', '601989.XSHG'])\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import talib\n",
    "data = pd.read_excel('sz50.xlsx',sheet_name=None,index_col='datetime')\n",
    "print(data.keys())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 题目二"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "[108.35  108.741 109.176 110.102 111.666]\n"
     ]
    }
   ],
   "source": [
    "closeArray = data['600036.XSHG']['close'].values\n",
    "print(type(closeArray))\n",
    "ma10=talib.SMA(closeArray,timeperiod=10)\n",
    "print(ma10[ma10.size-5:ma10.size])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 题目三"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "ma10Series = pd.DataFrame(ma10,index=data['600036.XSHG'].index)\n",
    "plt.figure(figsize=(15, 7))\n",
    "plt.plot(data['600036.XSHG'].index,data['600036.XSHG']['close'])\n",
    "plt.plot(ma10Series.index,ma10Series)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'close'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3077\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3078\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3079\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'close'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-dec0d91f3e20>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mROCR100df\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdateIndex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0mcloseArray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'close'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m     \u001b[0mROCR100Array\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtalib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mROCR100\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcloseArray\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtimeperiod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mROCR100\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mROCR100Array\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2686\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2687\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2688\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2689\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2690\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2693\u001b[0m         \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2694\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2695\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2696\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2697\u001b[0m         \u001b[1;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m   2487\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2488\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2489\u001b[1;33m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2490\u001b[0m             \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2491\u001b[0m             \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m   4113\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4114\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4115\u001b[1;33m                 \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4116\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4117\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3078\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3079\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3080\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3081\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3082\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'close'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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